minor update

set -x

export VLLM_ATTENTION_BACKEND=XFORMERS

export PYTHONHASHSEED=0

python -m vagen.env.sokoban.create_dataset \

    --data_dir data/sokoban-text-1-step \

    --max_action_length 1 \

    --dim_room 6 6 \

    --num_boxes 1 \

    --max_steps 100 \

    --search_depth 30 \

    --start_seed 0 \

    --train_ratio 0.8 \

    --max_action_per_step 1 \

    --max_action_penalty -0.1 \

    --format_reward 0.5 \

    --n_candidate 20000 \

    --force-gen

if [ $? -ne 0 ]; then

    echo "Failed to generate dataset"

    exit 1

fi

# max_trajectory_length = max_prompt_length + max_response_length

python3 -m vagen.trainer.main_ppo \

    algorithm.adv_estimator=gae \

    algorithm.high_level_gamma=0.95 \

    data.train_files=data/sokoban-text-1-step/train.parquet \

    data.val_files=data/sokoban-text-1-step/test.parquet \

    data.train_batch_size=16 \

    data.max_prompt_length=768 \

    data.max_response_length=128 \

    data.max_trajectory_length=1024 \

    data.image_key=images \

    actor_rollout_ref.model.path=Qwen/Qwen2.5-0.5B-Instruct \

    actor_rollout_ref.actor.optim.lr=1e-6 \

    actor_rollout_ref.model.use_remove_padding=False \

    actor_rollout_ref.actor.ppo_mini_batch_size=32 \

    actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu=4 \

    actor_rollout_ref.actor.use_kl_loss=False \

    actor_rollout_ref.actor.kl_loss_coef=0.001 \

    actor_rollout_ref.actor.kl_loss_type=low_var_kl \

    actor_rollout_ref.model.enable_gradient_checkpointing=True \

    actor_rollout_ref.actor.fsdp_config.param_offload=False \

    actor_rollout_ref.actor.fsdp_config.optimizer_offload=False \

    actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu=4 \

    actor_rollout_ref.rollout.tensor_model_parallel_size=1 \

    actor_rollout_ref.rollout.name=vllm \

    actor_rollout_ref.rollout.gpu_memory_utilization=0.4 \

    actor_rollout_ref.rollout.enable_chunked_prefill=False \

    actor_rollout_ref.rollout.enforce_eager=False \

    actor_rollout_ref.rollout.free_cache_engine=False \

    actor_rollout_ref.rollout.n=1 \

    actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu=4 \

    actor_rollout_ref.ref.fsdp_config.param_offload=True \

    critic.optim.lr=1e-5 \

    critic.model.use_remove_padding=False \

    critic.model.path=Qwen/Qwen2.5-0.5B-Instruct \

    critic.model.enable_gradient_checkpointing=True \

    critic.ppo_micro_batch_size_per_gpu=4 \

    critic.model.fsdp_config.param_offload=False \

    critic.model.fsdp_config.optimizer_offload=False \

    algorithm.kl_ctrl.kl_coef=0.001 \

    trainer.critic_warmup=0 \

    trainer.logger=['console','wandb'] \

    trainer.project_name='vagen' \

    trainer.experiment_name='debug_single_action_single_turn_ppo' \

    trainer.n_gpus_per_node=1 \

    trainer.nnodes=1 \

    trainer.save_freq=100 \

    trainer.test_freq=5 \

    trainer.total_epochs=15 \

    rollout_manager.max_turns=1 \

    rollout_manager.window_size=5 \

    trainer.val_before_train=True \

    trainer.val_generations_to_log_to_wandb=8 \

    rollout_manager.n_trajectory=8 \

    2>&1 | tee debug_single_action_single_turn_ppo.log

                        image_data.append(img)

        prompt_with_chat_template = self.tokenizer.apply_chat_template(chat, add_generation_prompt=(not is_final), tokenize=False)

        if is_final: # NoTE hard coded

            assert prompt_with_chat_template[-1]== '\n', "The last token should be new line token"

        if is_final: # NOTE hard coded

            assert prompt_with_chat_template[-1] == '\n', f"The last token should be new line token, got {prompt_with_chat_template[-1]}"

            prompt_with_chat_template = prompt_with_chat_template[:-1] # remove the last in token

        # switch box_end and im_end so that the model can learn to generate <|im_end|>

        if is_final:

            assert prompt_with_chat_template[-1] == '\n', "The last token of the prompt should be a newline"

            prompt_with_chat_template = prompt_with_chat_template[:-1]

        prompt_with_chat_template = prompt_with_chat_template.replace(

            f'{self.config.special_token_for_loss_mask[1]}{self.tokenizer.eos_token}',

            f'{self.tokenizer.eos_token}{self.config.special_token_for_loss_mask[1]}')